Image Denoising Via Collaborative Support-Agnostic Recovery

TL;DR

This paper introduces a new image denoising method that leverages collaborative support-agnostic sparse reconstruction, grouping similar patches to improve support estimation and achieve superior restoration quality.

Contribution

It presents a novel collaborative denoising algorithm that improves support estimation by sharing information among similar patches, enhancing image restoration performance.

Findings

Outperforms state-of-the-art algorithms in SSIM and PSNR.

Effective support estimation through collaborative patch processing.

Demonstrates significant improvements in image quality metrics.

Abstract

In this paper, we propose a novel image denoising algorithm using collaborative support-agnostic sparse reconstruction. An observed image is first divided into patches. Similarly structured patches are grouped together to be utilized for collaborative processing. In the proposed collaborative schemes, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group. This provides very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of SSIM and PSNR, demonstrate the superiority of the proposed algorithm.